

import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference






class 			UpperCAmelCase__						(			_UpperCAmelCase		,	unittest.TestCase			):






  """simple docstring"""


  a 	=   ShapEImgaImgPipeline
  a 	=   ['image']
  a 	=   ['image']
  a 	=   [
      'num_images_per_prompt',
      'num_inference_steps',
      'generator',
      'latents',
      'guidance_scale',
      'frame_size',
      'output_type',
      'return_dict',
  ]
  a 	=   False






  @property
  def  lowercase_						(				self		:						str   )						->				Any:
       return 32






  @property
  def  lowercase_						(				self		:						int   )						->				Optional[int]:
       return 32






  @property
  def  lowercase_						(				self		:						Optional[Any]   )						->				str:
       return self.time_input_dim * 4






  @property
  def  lowercase_						(				self		:						Optional[int]   )						->				Optional[int]:
       return 8






  @property
  def  lowercase_						(				self		:						Union[str, Any]   )						->				Tuple:
       torch.manual_seed(0   )
       SCREAMING_SNAKE_CASE__       =       CLIPVisionConfig(
           hidden_size=self.text_embedder_hidden_size      ,  image_size=64      ,  projection_dim=self.text_embedder_hidden_size      ,  intermediate_size=37      ,  num_attention_heads=4      ,  num_channels=3      ,  num_hidden_layers=5      ,  patch_size=1      ,  )

       SCREAMING_SNAKE_CASE__       =       CLIPVisionModel(__UpperCamelCase   )
       return model






  @property
  def  lowercase_						(				self		:						Optional[int]   )						->				Optional[Any]:
       SCREAMING_SNAKE_CASE__       =       CLIPImageProcessor(
           crop_size=224      ,  do_center_crop=__UpperCamelCase      ,  do_normalize=__UpperCamelCase      ,  do_resize=__UpperCamelCase      ,  image_mean=[0.48145466, 0.4578275, 0.40821073]      ,  image_std=[0.26862954, 0.26130258, 0.27577711]      ,  resample=3      ,  size=224      ,  )

       return image_processor






  @property
  def  lowercase_						(				self		:						Union[str, Any]   )						->				Optional[Any]:
       torch.manual_seed(0   )

       SCREAMING_SNAKE_CASE__       =       {
           '''num_attention_heads''': 2,
           '''attention_head_dim''': 16,
           '''embedding_dim''': self.time_input_dim,
           '''num_embeddings''': 32,
           '''embedding_proj_dim''': self.text_embedder_hidden_size,
           '''time_embed_dim''': self.time_embed_dim,
           '''num_layers''': 1,
           '''clip_embed_dim''': self.time_input_dim * 2,
           '''additional_embeddings''': 0,
           '''time_embed_act_fn''': '''gelu''',
           '''norm_in_type''': '''layer''',
           '''embedding_proj_norm_type''': '''layer''',
           '''encoder_hid_proj_type''': None,
           '''added_emb_type''': None,
       }

       SCREAMING_SNAKE_CASE__       =       PriorTransformer(**__UpperCamelCase   )
       return model






  @property
  def  lowercase_						(				self		:						Dict   )						->				Union[str, Any]:
       torch.manual_seed(0   )

       SCREAMING_SNAKE_CASE__       =       {
           '''param_shapes''': (
               (self.renderer_dim, 93),
               (self.renderer_dim, 8),
               (self.renderer_dim, 8),
               (self.renderer_dim, 8),
           ),
           '''d_latent''': self.time_input_dim,
           '''d_hidden''': self.renderer_dim,
           '''n_output''': 12,
           '''background''': (
               0.1,
               0.1,
               0.1,
           ),
       }
       SCREAMING_SNAKE_CASE__       =       ShapERenderer(**__UpperCamelCase   )
       return model






  def  lowercase_						(				self		:						List[str]   )						->				Optional[Any]:
       SCREAMING_SNAKE_CASE__       =       self.dummy_prior
       SCREAMING_SNAKE_CASE__       =       self.dummy_image_encoder
       SCREAMING_SNAKE_CASE__       =       self.dummy_image_processor
       SCREAMING_SNAKE_CASE__       =       self.dummy_renderer

       SCREAMING_SNAKE_CASE__       =       HeunDiscreteScheduler(
           beta_schedule='''exp'''      ,  num_train_timesteps=1024      ,  prediction_type='''sample'''      ,  use_karras_sigmas=__UpperCamelCase      ,  clip_sample=__UpperCamelCase      ,  clip_sample_range=1.0      ,  )
       SCREAMING_SNAKE_CASE__       =       {
           '''prior''': prior,
           '''image_encoder''': image_encoder,
           '''image_processor''': image_processor,
           '''renderer''': renderer,
           '''scheduler''': scheduler,
       }

       return components






  def  lowercase_						(				self		:						int      ,  __lowerCamelCase		:						Tuple      ,  __lowerCamelCase		:						Tuple=0   )						->				Any:
       SCREAMING_SNAKE_CASE__       =       floats_tensor((1, 3, 64, 64)      ,  rng=random.Random(__UpperCamelCase   )   ).to(__UpperCamelCase   )

       if str(__UpperCamelCase   ).startswith('''mps'''   ):
            SCREAMING_SNAKE_CASE__       =       torch.manual_seed(__UpperCamelCase   )
       else:
            SCREAMING_SNAKE_CASE__       =       torch.Generator(device=__UpperCamelCase   ).manual_seed(__UpperCamelCase   )
       SCREAMING_SNAKE_CASE__       =       {
           '''image''': input_image,
           '''generator''': generator,
           '''num_inference_steps''': 1,
           '''frame_size''': 32,
           '''output_type''': '''np''',
       }
       return inputs






  def  lowercase_						(				self		:						Union[str, Any]   )						->				List[Any]:
       SCREAMING_SNAKE_CASE__       =       '''cpu'''

       SCREAMING_SNAKE_CASE__       =       self.get_dummy_components()

       SCREAMING_SNAKE_CASE__       =       self.pipeline_class(**__UpperCamelCase   )
       SCREAMING_SNAKE_CASE__       =       pipe.to(__UpperCamelCase   )

       pipe.set_progress_bar_config(disable=__UpperCamelCase   )

       SCREAMING_SNAKE_CASE__       =       pipe(**self.get_dummy_inputs(__UpperCamelCase   )   )
       SCREAMING_SNAKE_CASE__       =       output.images[0]
       SCREAMING_SNAKE_CASE__       =       image[0, -3:, -3:, -1]

       assert image.shape == (20, 32, 32, 3)

       SCREAMING_SNAKE_CASE__       =       np.array(
           [
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
           ]   )

       assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2






  def  lowercase_						(				self		:						Union[str, Any]   )						->				Tuple:
       self._test_inference_batch_consistent(batch_sizes=[1, 2]   )






  def  lowercase_						(				self		:						int   )						->				List[Any]:
       SCREAMING_SNAKE_CASE__       =       torch_device == '''cpu'''
       SCREAMING_SNAKE_CASE__       =       True
       self._test_inference_batch_single_identical(
           batch_size=2      ,  test_max_difference=__UpperCamelCase      ,  relax_max_difference=__UpperCamelCase      ,  )







  def  lowercase_						(				self		:						List[Any]   )						->				Union[str, Any]:
       SCREAMING_SNAKE_CASE__       =       self.get_dummy_components()
       SCREAMING_SNAKE_CASE__       =       self.pipeline_class(**__UpperCamelCase   )
       SCREAMING_SNAKE_CASE__       =       pipe.to(__UpperCamelCase   )
       pipe.set_progress_bar_config(disable=__UpperCamelCase   )

       SCREAMING_SNAKE_CASE__       =       1
       SCREAMING_SNAKE_CASE__       =       2

       SCREAMING_SNAKE_CASE__       =       self.get_dummy_inputs(__UpperCamelCase   )

       for key in inputs.keys():
            if key in self.batch_params:
                 SCREAMING_SNAKE_CASE__       =       batch_size * [inputs[key]]

       SCREAMING_SNAKE_CASE__       =       pipe(**__UpperCamelCase      ,  num_images_per_prompt=__UpperCamelCase   )[0]

       assert images.shape[0] == batch_size * num_images_per_prompt






@slow
@require_torch_gpu
class 			UpperCAmelCase__						(			unittest.TestCase			):






  """simple docstring"""


  def  lowercase_						(				self		:						List[Any]   )						->				Tuple:
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()







  def  lowercase_						(				self		:						str   )						->				Any:
       SCREAMING_SNAKE_CASE__       =       load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png'''   )
       SCREAMING_SNAKE_CASE__       =       load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/shap_e/test_shap_e_img2img_out.npy'''   )
       SCREAMING_SNAKE_CASE__       =       ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img'''   )
       SCREAMING_SNAKE_CASE__       =       pipe.to(__UpperCamelCase   )
       pipe.set_progress_bar_config(disable=__UpperCamelCase   )

       SCREAMING_SNAKE_CASE__       =       torch.Generator(device=__UpperCamelCase   ).manual_seed(0   )

       SCREAMING_SNAKE_CASE__       =       pipe(
           __UpperCamelCase      ,  generator=__UpperCamelCase      ,  guidance_scale=3.0      ,  num_inference_steps=64      ,  frame_size=64      ,  output_type='''np'''      ,  ).images[0]

       assert images.shape == (20, 64, 64, 3)

       assert_mean_pixel_difference(__UpperCamelCase      ,  __UpperCamelCase   )




"""simple docstring"""



import cva
import numpy as np




class 		a_    :







    def __init__(      self      :							Optional[Any]  ,       __UpperCamelCase      :							float  ,       __UpperCamelCase      :							int  )		->Dict:






        '''simple docstring'''






        if k in (0.0_4, 0.0_6):
            _UpperCAmelCase        = k
            _UpperCAmelCase        = window_size
        else:
            raise ValueError("""invalid k value"""  )







    def __str__(      self      :							Tuple  )		->str:






        '''simple docstring'''






        return str(self.k  )







    def   _snake_case       (      self      :							str  ,       __UpperCamelCase      :							str  )		->tuple[cva.Mat, list[list[int]]]:






        '''simple docstring'''






        _UpperCAmelCase        = cva.imread(__UpperCamelCase  ,       0  )
        _UpperCAmelCase     ,_UpperCAmelCase        = img.shape
        _UpperCAmelCase        = []
        _UpperCAmelCase        = img.copy()
        _UpperCAmelCase        = cva.cvtColor(__UpperCamelCase  ,       cva.COLOR_GRAY2RGB  )
        _UpperCAmelCase     ,_UpperCAmelCase        = np.gradient(__UpperCamelCase  )
        _UpperCAmelCase        = dx**2
        _UpperCAmelCase        = dy**2
        _UpperCAmelCase        = dx * dy
        _UpperCAmelCase        = 0.0_4
        _UpperCAmelCase        = self.window_size // 2
        for y in range(__UpperCamelCase  ,       h - offset  ):
            for x in range(__UpperCamelCase  ,       w - offset  ):
                _UpperCAmelCase        = ixx[
                    y - offset : y + offset + 1, x - offset : x + offset + 1
                ].sum()
                _UpperCAmelCase        = iyy[
                    y - offset : y + offset + 1, x - offset : x + offset + 1
                ].sum()
                _UpperCAmelCase        = ixy[
                    y - offset : y + offset + 1, x - offset : x + offset + 1
                ].sum()

                _UpperCAmelCase        = (wxx * wyy) - (wxy**2)
                _UpperCAmelCase        = wxx + wyy
                _UpperCAmelCase        = det - k * (trace**2)
                # Can change the value
                if r > 0.5:
                    corner_list.append([x, y, r]  )
                    color_img.itemset((y, x, 0)  ,       0  )
                    color_img.itemset((y, x, 1)  ,       0  )
                    color_img.itemset((y, x, 2)  ,       2_55  )
        return color_img, corner_list


if __name__ == "__main__":
 a       :						List[Any]       		=		HarrisCorner(0.04, 3)
 a		,			a       :						List[Any]       		=		edge_detect.detect('''path_to_image''')
 cva.imwrite('''detect.png''', color_img)